Project SPINDLE is about to end. As lead on the project here at the Academic IT services I’ve tried to summarise the main impact and benefits of the work:
- Training – improved skills within the OpenSpires and Media teams
- Discoverability -making media more discoverable and accessible,
- Content -the creation of better cataloguing resources, tools and data
- Knowledge exchange – through the documentation of the workflow and the creation of free to use open source tools helping others to build on our work
- Community building – working with others to explore ideas for time-coded texts and media
The project was funded by the JISC to rapidly innovate around technical issues that support the release of Open Educational Resources. The single biggest benefit of the project has been in training and skills acquisition for our media production team – by allowing time and funding to foster a multidisciplinary collaboration across linguistics, phonetics and computer science to research and create the prototype service. The fast-paced short five month project has achieved all of it’s original aims and through the efforts of combining our summer intern programmer with an expert in speech to text software we have manged to move beyond the area of keyword cataloging and create a more complex prototype web application to process transcripts as media is created. This captioning toolkit will speed up work, be very cost-effective and allow crowd-sourced corrections to be exported into emerging HTML5 captioning and archival formats.
Here is a list of the substantial benefits of the project:
- SPINDLE developed a round trip work flow for transcription correction and created over 20 blog reports evaluating this work.
- SPINDLE researched the use of automatic speech to text programs to generate transcriptions automatically. This automatic transcription serves as a starting point to create manual transcriptions and captions, as well as the base to generate keywords automatically.
- SPINDLE documented how to use Adobe Premiere to make transcripts and how a media unit might install the research toolkit CMU SPHINX 4 to transcribe podcasts – https://github.com/ox-it/spindle-code/tree/master/speechToText
- A large corpus of text – SPINDLE proved that the workflow could generate keywords automatically for 3,426 podcasts. Once these keywords are migrated into our delivery channels they will lead to better indexing and cataloguing, and better discoverability of our Open Educational Resources (OER) by search engines.
- Accessibility – We generated unchecked and uncorrected caption file data in WebVTT timecoded format for our OER video podcasts
- Archival formats – We investigated an archival format for the keywords and transcripts using the Text Encoding Initiative encoding format which also include OER licence information
We developed code:
- Programming scripts for finding non-common keywords from text transcripts – http://github.com/ox-it/spindle-code
- A new prototype online transcription editor – A toolkit that aids captioning work – freely available in a github code repository – http://github.com/ox-it/spindle-code
- Integrating the SPINDLE Caption Editor to CMU Sphinx, and to import Adobe Premiere XMP transcript files and investigated an API to the Koemi commercial web service
- To help accessibility via text and video caption formats – Exporting to plain text, HTML, Web VTT and a data RSS feed.
- Test the prototype software in a day to day production server environment
- Review and reduce any minor keyword cataloguing errors
- Ingest the cataloguing data into our main databases
- Expose the new cataloguing keywords on the 4,000+ media items delivered by the Academic IT Services in feeds and web pages – primarily Oxford on iTunesU and http://podcasts.ox.ac.uk
- Explore ways of filtering even further the keywords by ranking and removing words that are unlikely to be used in online searches
- Explore the practicalities and costs of crowd-sourcing the correction of raw automatic transcriptions of the lectures with the new caption software
- Explore using the benefits and weaknesses of automatic draft text as full text search
- Compare the costs of managing volunteers correcting automatic transcripts to the cost and accuracy of using a professional transcription service.
- Attitudes to OER text transcript release – information on contributor attitudes to displaying texts alongside a lecture.
- Policy for approval of texts
- Investigating storing a voice-bank or key subject terms database to help the software improve regular transcription
- Corpus Linguistics and language – SPINDLE offers a unique snapshot of text representing the academic language over a four year period at Oxford.
- English as a foreign language – There has been interest and debate by the language learning community on SPINDLE and captioning lectures here – http://chirpstory.com/li/25724
- Media Production Services – there is interest in using the SPINDLE work within automatic lecture capture solutions- http://opencast.org
- Translation of texts to foreign languages
- Data mining – research across the disciplines